Browsing by Subject "food contaminants"

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    Leveraging Linear Polymer Affinity Agents and Surface-enhanced Raman Scattering for the Detection of Food Contaminants
    (2022-04) Rodriguez, Rebeca
    This thesis focuses on leveraging linear polymer affinity agents and surface- enhanced Raman scattering (SERS) for the detection of food contaminants. First, I discuss the different sensing techniques and methodology that exist for food contamination detection: UV-visible spectroscopy, immuno- and lateral flow assays, liquid and gas chromatography, field-effect transistors, and SERS. I address the need for relatively facile and inexpensive multiplex detection and how linear polymer affinity agents can address these needs. The first experimental work focuses on optimization of SERS substrates for biosensing applications and initial work with anchored polymer chain lengths for the detection of the food allergen and protein soybean agglutinin with a glycopolymer. I then focus on optimization of linear polymer affinity agents for the detection of mycotoxins, which are small molecule food toxins that are naturally produced by fungi. I determined that attachment order, attachment chemistry, and polymer chain length all play a role in small molecule sensing. These optimization studies led me to be able to do multiplex detection of two small molecule toxins with linear polymer affinity agents and formulate conclusions on how polymers and small molecules bind through hydrogen bonding. I did this by combining SERS experimental studies and computational modeling of these small molecules to label what vibrational modes are being observed in the multiplexed spectra. In an effort to use linear polymer affinity agents for another class of food contaminants, bacteria, we work to optimize and use a linear glycopolymer for the detection of Listeria monocytogenes. Although the previous work with small molecules concluded that small to mid-length polymers performed best for capture and detection, this work has shown that longer polymer chain lengths work best to promote binding between polymer and Listeria. This gives insight on how to move forward with linear polymer affinity-enabled detection of different classes of food contaminants and pathogens. Overall, this work demonstrates optimization of SERS sensing to achieve limits of detection comparable to current detection methods with a simpler and more flexible signal transduction mechanism, providing an opportunity for future applications to multiplex at low-cost.